Sr Machine Learning Engineer

PayPal PayPal · Fintech · San Jose, CA +1 · Machine Learning Engineering

Senior Machine Learning Engineer at PayPal in San Jose, CA, focusing on developing and deploying scalable ML algorithms for payment fraud detection. Requires a Doctorate or Master's degree with relevant experience in ML frameworks, graph-based ML, and applied research.

What you'd actually do

  1. Conduct cutting-edge research in machine learning to develop solutions that address complex business challenges, focusing on payment fraud detection.
  2. Analyze large, complex data sets to extract actionable insights that inform business strategies and decision-making.
  3. Design, develop, and deploy scalable machine learning algorithms, integrating them into PayPal’s AI/ML systems.
  4. Experiment with innovative models and new approaches to enhance fraud detection capabilities.
  5. Collaborate with cross-functional and international teams to align technical solutions and contribute to the development of state-of-the-art technologies.

Skills

Required

  • TensorFlow
  • PyTorch
  • scikit-learn
  • machine learning workflow management
  • experiment-tracking tools
  • Python
  • Graph-based machine learning
  • graph neural networks
  • graph data modeling
  • analysis
  • research methodology
  • documentation
  • scientific communication
  • applied machine learning
  • Algorithm optimization
  • computational efficiency
  • system-level integration
  • scalable machine learning research
  • calculus
  • linear algebra
  • matrix theory
  • information theory
  • optimization
  • network science
  • Cloud computing
  • high-performance computing
  • model interpretability
  • performance analysis
  • predictive modeling
  • unsupervised learning
  • self-supervised learning
  • pattern recognition
  • representation learning
  • formulating modeling objectives
  • designing and implementing algorithms
  • conducting experiments
  • evaluating results
  • iteratively refining models
  • relational machine learning techniques
  • data preprocessing
  • feature engineering
  • statistical validation
  • model robustness
  • generalization
  • structured model-training workflows
  • performance tuning
  • retraining
  • experiment management
  • reproducible results
  • research prototypes
  • ablation studies
  • sensitivity studies

What the JD emphasized

  • payment fraud detection
  • scalable machine learning algorithms
  • enhance fraud detection capabilities
  • state-of-the-art technologies

Other signals

  • fraud detection
  • scalable machine learning algorithms
  • enhance fraud detection capabilities
  • advancement of AI/ML technologies